ASI Masterclass - Workshop Session 3
Thomas Ashhurst, Givanna Putri, Felix Marsh-Wakefield, Jennifer Habel, Wuji Zhang
26/10/2021
SESSION 3 - respiratory (3:00 pm)
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Session #3: High-dimensional analysis of immune responses to COVID-19 in the respiratory tract Tue 26-Oct, 3:00 pm – 4:30 pm Lead instructors: Givanna Putri, Wuji Zhang In this session we will be investigating the immune response to COVID-19 in the respiratory tract, from samples acquired via an endotracheal tube. In particular we will explore a variety of myeloid cells that infiltrate the respiratory tract via the blood, including macrophages and neutrophils, and how they compare to similar cells in the blood.
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Open R script
#######################################################################################################
#### 1. Load packages, and set working directory
####################################################################################################### ### Load libraries
library(Spectre)
Spectre::package.check() # Check that all required packages are installed
Spectre::package.load() # Load required packages ### Set PrimaryDirectory
dirname(rstudioapi::getActiveDocumentContext()$path) # Finds the directory where this script is located
setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) # Sets the working directory to where the script is located
getwd()
PrimaryDirectory <- getwd()
PrimaryDirectory ### Set 'input' directory
setwd(PrimaryDirectory)
setwd("data/")
InputDirectory <- getwd()
setwd(PrimaryDirectory) ### Set 'metadata' directory
setwd(PrimaryDirectory)
setwd("metadata/")
MetaDirectory <- getwd()
setwd(PrimaryDirectory) ### Create output directory
dir.create("Output_Spectre", showWarnings = FALSE)
setwd("Output_Spectre")
OutputDirectory <- getwd()
setwd(PrimaryDirectory)#######################################################################################################
#### 2. Import and prep data
####################################################################################################### ### Import data
setwd(InputDirectory)
list.files(InputDirectory, ".csv")
data.list <- Spectre::read.files(file.loc = InputDirectory,
file.type = ".csv",
do.embed.file.names = TRUE) ### Check the data
check <- do.list.summary(data.list)
check$name.table # Review column names and their subsequent values
check$ncol.check # Review number of columns (features, markers) in each sample
check$nrow.check # Review number of rows (cells) in each sample
data.list[[1]] ### Merge data
cell.dat <- Spectre::do.merge.files(dat = data.list)
cell.dat ### Read in metadata
setwd(MetaDirectory)
meta.dat <- fread("sample.details.csv")
meta.dat#######################################################################################################
#### 3. Data transformation
####################################################################################################### setwd(OutputDirectory)
dir.create("Output 1 - transformed plots")
setwd("Output 1 - transformed plots") ### Arcsinh transformation
as.matrix(names(cell.dat))
low.asinh <- names(cell.dat)[c(3:19)]
low.asinh
high.cofactor <- 5000
low.cofactor <- 1000 plot.against <- "CD66b_asinh"
cell.dat <- do.asinh(cell.dat, low.asinh, cofactor = low.cofactor)
transformed.cols <- c(paste0(low.asinh, "_asinh"))
transformed.cols
sub <- do.subsample(cell.dat, 20000) for(i in transformed.cols){
make.colour.plot(sub, i, plot.against)
} ### Re-scale FSC SSC
cell.dat <- do.rescale(cell.dat, c('FSC-A', 'SSC-A'), new.min = 0, new.max = 4)
cell.dat#######################################################################################################
#### 4. Add metadata and set some preferences
####################################################################################################### ### Add metadata to data.table
meta.dat
sample.info <- meta.dat[,c(1:3)]
sample.info
cell.dat <- do.add.cols(cell.dat, "FileName", sample.info, "FileName", rmv.ext = TRUE)
cell.dat ### Columns
as.matrix(names(cell.dat))
cellular.cols <- names(cell.dat)[c(22:40)]
as.matrix(cellular.cols)
as.matrix(names(cell.dat))
cluster.cols <- names(cell.dat)[c(22:40)]
as.matrix(cluster.cols)
exp.name <- "COVID19 respiratory"
sample.col <- "Sample"
group.col <- "Group" ### Re-order data
as.matrix(unique(cell.dat[[group.col]]))
cell.dat <- do.reorder(cell.dat, sample.col, c('Respiratory_1', 'Respiratory_2', 'Respiratory_3'))
cell.dat#######################################################################################################
#### 5. Clustering and dimensionality reduction
####################################################################################################### setwd(OutputDirectory)
dir.create("Output 2 - clustering")
setwd("Output 2 - clustering") ### Clustering
cell.dat <- run.flowsom(cell.dat, cluster.cols, meta.k = 40)
cell.dat ### Dimensionality reduction
cell.sub <- do.subsample(cell.dat, c(2000, 10000, 20000), sample.col)
cell.sub
cell.sub <- run.fitsne(cell.sub, cluster.cols, perplexity = 200)
cell.sub ### DR plots
make.colour.plot(cell.sub, "FItSNE_X", "FItSNE_Y", "FlowSOM_metacluster", col.type = 'factor', add.label = TRUE) make.multi.plot(cell.sub, "FItSNE_X", "FItSNE_Y", cellular.cols, figure.title = 'Cellular cols') make.multi.plot(cell.sub, "FItSNE_X", "FItSNE_Y", cluster.cols, figure.title = 'Cluster cols') make.multi.plot(cell.sub, "FItSNE_X", "FItSNE_Y", "FlowSOM_metacluster", sample.col, col.type = 'factor') make.multi.plot(cell.sub, "CD16_asinh", "CD66b_asinh", "FlowSOM_metacluster", sample.col, col.type = 'factor', figure.title = 'CD66b x CD16')